Overview

Dataset statistics

Number of variables16
Number of observations8998
Missing cells552
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory128.0 B

Variable types

NUM11
CAT4
BOOL1

Warnings

income is highly correlated with ageHigh correlation
age is highly correlated with incomeHigh correlation
mnt is highly correlated with frqHigh correlation
frq is highly correlated with mntHigh correlation
dependents has 282 (3.1%) missing values Missing
status has 177 (2.0%) missing values Missing
kitchen has 833 (9.3%) zeros Zeros
toys has 815 (9.1%) zeros Zeros
house_keeping has 851 (9.5%) zeros Zeros

Reproduction

Analysis started2020-10-12 08:45:42.683345
Analysis finished2020-10-12 08:46:11.281520
Duration28.6 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct61
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1966.05968
Minimum1936
Maximum1996
Zeros0
Zeros (%)0.0%
Memory size70.3 KiB
2020-10-12T09:46:11.421567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1936
5-th percentile1939
Q11951
median1966
Q31981
95-th percentile1993
Maximum1996
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.29655221
Coefficient of variation (CV)0.008797572313
Kurtosis-1.195990117
Mean1966.05968
Median Absolute Deviation (MAD)15
Skewness0.007954084276
Sum17690605
Variance299.1707182
MonotocityNot monotonic
2020-10-12T09:46:11.590927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19741711.9%
 
19511691.9%
 
19921681.9%
 
19791671.9%
 
19761661.8%
 
19611651.8%
 
19601641.8%
 
19491631.8%
 
19591621.8%
 
19781601.8%
 
Other values (51)734381.6%
 
ValueCountFrequency (%) 
1936750.8%
 
19371401.6%
 
19381461.6%
 
19391371.5%
 
19401531.7%
 
ValueCountFrequency (%) 
1996810.9%
 
19951471.6%
 
19941591.8%
 
19931571.7%
 
19921681.9%
 

income
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8524
Distinct (%)95.2%
Missing46
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean69963.55083
Minimum10000
Maximum140628
Zeros0
Zeros (%)0.0%
Memory size70.3 KiB
2020-10-12T09:46:11.761201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile26314.6
Q147741
median70030.5
Q392218
95-th percentile113395.3
Maximum140628
Range130628
Interquartile range (IQR)44477

Descriptive statistics

Standard deviation27591.55623
Coefficient of variation (CV)0.3943704386
Kurtosis-0.9293280359
Mean69963.55083
Median Absolute Deviation (MAD)22214.5
Skewness0.008688890946
Sum626313707
Variance761293975
MonotocityNot monotonic
2020-10-12T09:46:11.940674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10000350.4%
 
641854< 0.1%
 
834553< 0.1%
 
335423< 0.1%
 
994523< 0.1%
 
661843< 0.1%
 
379023< 0.1%
 
397823< 0.1%
 
517433< 0.1%
 
499483< 0.1%
 
Other values (8514)888998.8%
 
(Missing)460.5%
 
ValueCountFrequency (%) 
10000350.4%
 
101821< 0.1%
 
101861< 0.1%
 
106081< 0.1%
 
108861< 0.1%
 
ValueCountFrequency (%) 
1406281< 0.1%
 
1373381< 0.1%
 
1370531< 0.1%
 
1369221< 0.1%
 
1362131< 0.1%
 

frq
Real number (ℝ≥0)

HIGH CORRELATION

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.84807735
Minimum3
Maximum59
Zeros0
Zeros (%)0.0%
Memory size70.3 KiB
2020-10-12T09:46:12.121832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q110
median17
Q328
95-th percentile40
Maximum59
Range56
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.90343461
Coefficient of variation (CV)0.549344625
Kurtosis-0.4139889171
Mean19.84807735
Median Absolute Deviation (MAD)8
Skewness0.6977790699
Sum178593
Variance118.8848863
MonotocityNot monotonic
2020-10-12T09:46:12.287224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
106357.1%
 
95836.5%
 
115135.7%
 
84935.5%
 
124184.6%
 
73253.6%
 
133163.5%
 
142823.1%
 
212382.6%
 
252332.6%
 
Other values (47)496255.1%
 
ValueCountFrequency (%) 
350.1%
 
4240.3%
 
5871.0%
 
61731.9%
 
73253.6%
 
ValueCountFrequency (%) 
592< 0.1%
 
581< 0.1%
 
571< 0.1%
 
563< 0.1%
 
553< 0.1%
 

rcn
Real number (ℝ≥0)

Distinct378
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.46977106
Minimum0
Maximum549
Zeros44
Zeros (%)0.5%
Memory size70.3 KiB
2020-10-12T09:46:12.654022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q126
median53
Q379
95-th percentile99
Maximum549
Range549
Interquartile range (IQR)53

Descriptive statistics

Standard deviation69.76180219
Coefficient of variation (CV)1.116728956
Kurtosis21.09692287
Mean62.46977106
Median Absolute Deviation (MAD)26
Skewness4.174006567
Sum562103
Variance4866.709045
MonotocityNot monotonic
2020-10-12T09:46:13.016031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
91071.2%
 
561051.2%
 
641031.1%
 
41021.1%
 
291021.1%
 
271001.1%
 
921001.1%
 
54991.1%
 
68991.1%
 
17981.1%
 
Other values (368)798388.7%
 
ValueCountFrequency (%) 
0440.5%
 
1911.0%
 
2921.0%
 
3911.0%
 
41021.1%
 
ValueCountFrequency (%) 
5493< 0.1%
 
5471< 0.1%
 
5463< 0.1%
 
5422< 0.1%
 
5401< 0.1%
 

mnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct717
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean622.162814
Minimum6
Maximum3052
Zeros0
Zeros (%)0.0%
Memory size70.3 KiB
2020-10-12T09:46:13.358850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q163
median383
Q31076
95-th percentile1917.15
Maximum3052
Range3046
Interquartile range (IQR)1013

Descriptive statistics

Standard deviation646.7682046
Coefficient of variation (CV)1.039548154
Kurtosis-0.05809376933
Mean622.162814
Median Absolute Deviation (MAD)343
Skewness0.9809806035
Sum5598221
Variance418309.1104
MonotocityNot monotonic
2020-10-12T09:46:13.552920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
191581.8%
 
411211.3%
 
201081.2%
 
40891.0%
 
42881.0%
 
64861.0%
 
66780.9%
 
65760.8%
 
92610.7%
 
118560.6%
 
Other values (707)807789.8%
 
ValueCountFrequency (%) 
61< 0.1%
 
72< 0.1%
 
880.1%
 
9140.2%
 
10220.2%
 
ValueCountFrequency (%) 
30521< 0.1%
 
29381< 0.1%
 
29361< 0.1%
 
28781< 0.1%
 
28231< 0.1%
 

clothes
Real number (ℝ≥0)

Distinct99
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.44665481
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Memory size70.3 KiB
2020-10-12T09:46:13.853764image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q133
median51
Q369
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.42224892
Coefficient of variation (CV)0.4642973653
Kurtosis-0.9185954232
Mean50.44665481
Median Absolute Deviation (MAD)18
Skewness-0.07821931254
Sum453919
Variance548.6017444
MonotocityNot monotonic
2020-10-12T09:46:14.215072image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
551501.7%
 
401411.6%
 
411391.5%
 
471391.5%
 
701371.5%
 
561361.5%
 
581361.5%
 
461351.5%
 
311331.5%
 
571331.5%
 
Other values (89)761984.7%
 
ValueCountFrequency (%) 
12< 0.1%
 
2150.2%
 
3240.3%
 
4380.4%
 
5440.5%
 
ValueCountFrequency (%) 
991< 0.1%
 
981< 0.1%
 
9790.1%
 
96170.2%
 
95250.3%
 

kitchen
Real number (ℝ≥0)

ZEROS

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.039675483
Minimum0
Maximum75
Zeros833
Zeros (%)9.3%
Memory size70.3 KiB
2020-10-12T09:46:14.396132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310
95-th percentile23
Maximum75
Range75
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.84813931
Coefficient of variation (CV)1.114843906
Kurtosis5.619265964
Mean7.039675483
Median Absolute Deviation (MAD)3
Skewness2.049458185
Sum63343
Variance61.59329064
MonotocityNot monotonic
2020-10-12T09:46:14.731103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1132614.7%
 
2100511.2%
 
08339.3%
 
37618.5%
 
46447.2%
 
55846.5%
 
65065.6%
 
74044.5%
 
83724.1%
 
102893.2%
 
Other values (48)227425.3%
 
ValueCountFrequency (%) 
08339.3%
 
1132614.7%
 
2100511.2%
 
37618.5%
 
46447.2%
 
ValueCountFrequency (%) 
751< 0.1%
 
671< 0.1%
 
651< 0.1%
 
611< 0.1%
 
591< 0.1%
 

small_appliances
Real number (ℝ≥0)

Distinct73
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.52411647
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Memory size70.3 KiB
2020-10-12T09:46:15.183490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q119
median28
Q337
95-th percentile50
Maximum74
Range73
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.5864368
Coefficient of variation (CV)0.4412559742
Kurtosis-0.4230030191
Mean28.52411647
Median Absolute Deviation (MAD)9
Skewness0.3146456491
Sum256660
Variance158.4183913
MonotocityNot monotonic
2020-10-12T09:46:15.363781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
232863.2%
 
222773.1%
 
262763.1%
 
272693.0%
 
192683.0%
 
252642.9%
 
302622.9%
 
282612.9%
 
312542.8%
 
292362.6%
 
Other values (63)634570.5%
 
ValueCountFrequency (%) 
11< 0.1%
 
22< 0.1%
 
3130.1%
 
4390.4%
 
5530.6%
 
ValueCountFrequency (%) 
742< 0.1%
 
731< 0.1%
 
721< 0.1%
 
701< 0.1%
 
693< 0.1%
 

toys
Real number (ℝ≥0)

ZEROS

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.036897088
Minimum0
Maximum62
Zeros815
Zeros (%)9.1%
Memory size70.3 KiB
2020-10-12T09:46:15.565864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310
95-th percentile23
Maximum62
Range62
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.924421742
Coefficient of variation (CV)1.126124433
Kurtosis5.644657211
Mean7.036897088
Median Absolute Deviation (MAD)3
Skewness2.096047402
Sum63318
Variance62.79645995
MonotocityNot monotonic
2020-10-12T09:46:15.810089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1137015.2%
 
298811.0%
 
08159.1%
 
37798.7%
 
46757.5%
 
55426.0%
 
64995.5%
 
74094.5%
 
83443.8%
 
92953.3%
 
Other values (48)228225.4%
 
ValueCountFrequency (%) 
08159.1%
 
1137015.2%
 
298811.0%
 
37798.7%
 
46757.5%
 
ValueCountFrequency (%) 
621< 0.1%
 
611< 0.1%
 
602< 0.1%
 
571< 0.1%
 
561< 0.1%
 

house_keeping
Real number (ℝ≥0)

ZEROS

Distinct59
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.929984441
Minimum0
Maximum77
Zeros851
Zeros (%)9.5%
Memory size70.3 KiB
2020-10-12T09:46:16.003357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q39
95-th percentile23
Maximum77
Range77
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.882655355
Coefficient of variation (CV)1.137470859
Kurtosis6.885521741
Mean6.929984441
Median Absolute Deviation (MAD)3
Skewness2.229124081
Sum62356
Variance62.13625544
MonotocityNot monotonic
2020-10-12T09:46:16.182747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1132614.7%
 
298110.9%
 
08519.5%
 
38489.4%
 
46757.5%
 
55195.8%
 
64775.3%
 
74465.0%
 
83574.0%
 
93093.4%
 
Other values (49)220924.5%
 
ValueCountFrequency (%) 
08519.5%
 
1132614.7%
 
298110.9%
 
38489.4%
 
46757.5%
 
ValueCountFrequency (%) 
771< 0.1%
 
721< 0.1%
 
621< 0.1%
 
591< 0.1%
 
582< 0.1%
 

dependents
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing282
Missing (%)3.1%
Memory size70.3 KiB
1
6164 
0
2552 
(Missing)
 
282
ValueCountFrequency (%) 
1616468.5%
 
0255228.4%
 
(Missing)2823.1%
 
2020-10-12T09:46:16.321589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

per_net_purchase
Real number (ℝ≥0)

Distinct82
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.42898422
Minimum4
Maximum88
Zeros0
Zeros (%)0.0%
Memory size70.3 KiB
2020-10-12T09:46:16.447670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q128
median45
Q357
95-th percentile69
Maximum88
Range84
Interquartile range (IQR)29

Descriptive statistics

Standard deviation18.49574245
Coefficient of variation (CV)0.4359223486
Kurtosis-1.03466056
Mean42.42898422
Median Absolute Deviation (MAD)14
Skewness-0.2664532226
Sum381776
Variance342.0924887
MonotocityNot monotonic
2020-10-12T09:46:16.668882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
562152.4%
 
542142.4%
 
572122.4%
 
551992.2%
 
581922.1%
 
611922.1%
 
531892.1%
 
131882.1%
 
601852.1%
 
591842.0%
 
Other values (72)702878.1%
 
ValueCountFrequency (%) 
41< 0.1%
 
53< 0.1%
 
6150.2%
 
7350.4%
 
8540.6%
 
ValueCountFrequency (%) 
881< 0.1%
 
841< 0.1%
 
831< 0.1%
 
823< 0.1%
 
813< 0.1%
 

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.3 KiB
M
5784 
F
3214 
ValueCountFrequency (%) 
M578464.3%
 
F321435.7%
 
2020-10-12T09:46:16.841574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-12T09:46:16.923594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:17.014739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

education
Categorical

Distinct6
Distinct (%)0.1%
Missing47
Missing (%)0.5%
Memory size70.3 KiB
Graduation
4429 
2nd Cycle
1496 
Master
1303 
1st Cycle
1104 
PhD
593 
ValueCountFrequency (%) 
Graduation442949.2%
 
2nd Cycle149616.6%
 
Master130314.5%
 
1st Cycle110412.3%
 
PhD5936.6%
 
OldSchool260.3%
 
(Missing)470.5%
 
2020-10-12T09:46:17.144846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-12T09:46:17.238722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:17.459558image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length9
Mean length8.631029118
Min length3

status
Categorical

MISSING

Distinct6
Distinct (%)0.1%
Missing177
Missing (%)2.0%
Memory size70.3 KiB
Married
3273 
Single
2293 
Together
2118 
Divorced
677 
Widow
445 
ValueCountFrequency (%) 
Married327336.4%
 
Single229325.5%
 
Together211823.5%
 
Divorced6777.5%
 
Widow4454.9%
 
Whatever150.2%
 
(Missing)1772.0%
 
2020-10-12T09:46:17.654037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-12T09:46:17.815999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:17.956967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length6.879862192
Min length3

description
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size70.3 KiB
OK nice!
3434 
Meh...
2107 
Kind of OK
2090 
Take my money!!
1326 
Horrible
 
41
ValueCountFrequency (%) 
OK nice!343438.2%
 
Meh...210723.4%
 
Kind of OK209023.2%
 
Take my money!!132614.7%
 
Horrible410.5%
 
2020-10-12T09:46:18.126898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-12T09:46:18.275982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:18.398024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length8
Mean length9.027783952
Min length6

Interactions

2020-10-12T09:45:43.615522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:43.783201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:43.955564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:44.113306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:44.258111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:44.624410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:44.794553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:44.942398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:45.086761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:45.226807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:45.374004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:45.506757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:45.653752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:45.832754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:45.978440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:46.137912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:46.309440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:46.447805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:46.617575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:46.775856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:46.950041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:47.122623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:47.266537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:47.418824image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:47.575279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:47.708543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:48.215826image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:48.377870image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:48.512364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:48.774613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:48.948934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:49.096653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:49.241332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:49.376590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:49.512637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:49.652468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:49.789572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:49.921937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:50.061747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:50.365671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:50.557550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:50.787941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:50.932271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:51.107990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:51.249602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:51.417035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:51.567503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:52.075036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:52.595948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:52.890036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:53.044380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:53.430170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:53.796272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:53.962217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:54.127383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:54.330140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:54.608705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:55.073590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:55.259451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:55.849942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:56.134774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:56.337074image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:56.641933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:56.864851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:57.009417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:57.303417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:57.485180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:57.640037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:57.803708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:57.956437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:58.106482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:58.349372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:58.520673image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:58.677910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:58.850331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:59.179932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:59.511830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:59.751924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:45:59.924655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:00.128926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:00.377340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:00.659129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:00.823413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:01.114847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:01.301806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:01.460435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:01.607325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:01.834052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:02.110893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:02.255934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:02.510313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:02.744360image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:02.921309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:03.085799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:03.305586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:03.477464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:03.637180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:03.783296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:03.945124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:04.310131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:04.518088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:04.810377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:05.081997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:05.426761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:05.785688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:06.029494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:06.287721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:06.646022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:06.961103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:07.177121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:07.325117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:07.454435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:07.677680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:08.014827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:08.155272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:08.475316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:08.611379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:08.754265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:09.069529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:09.290171image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:09.435212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-10-12T09:46:18.552833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Missing values

2020-10-12T09:46:09.732401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:10.574810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:10.830108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-10-12T09:46:11.007645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

ageincomefrqrcnmntclotheskitchensmall_appliancestoyshouse_keepingdependentsper_net_purchasegendereducationstatusdescription
0194690782.033661402375441030.019MGraduationTogetherTake my money!!
11936113023.0326153755138420.09FPhDDivorcedTake my money!!
2199028344.01169443219241241.059MGraduationMarriedKind of OK
3195593571.02610888601019651.035FMasterNaNOK nice!
4195591852.03126113859528441.034FGraduationTogetherTake my money!!
5198222386.014655647248211.067MPhDSingleOK nice!
6196969485.0187334571713181.046MGraduationTogetherOK nice!
7196068602.05444184112201.037MGraduationTogetherHorrible
81940109499.03075140138935990.017MGraduationDivorcedOK nice!
9199423846.08153191855171011.039F1st CycleTogetherMeh...

Last rows

ageincomefrqrcnmntclotheskitchensmall_appliancestoyshouse_keepingdependentsper_net_purchasegendereducationstatusdescription
89881947100928.0286115274320120.029FMasterDivorcedTake my money!!
8989194787605.02118823342193510.09M1st CycleWidowKind of OK
8990199528144.01041461140242221.059M1st CycleMarriedOK nice!
89911939126254.04636223132447980.022MGraduationDivorcedTake my money!!
8992195487399.02518375682781NaN47MGraduationMarriedKind of OK
8993196094367.028189668521341.055F1st CycleSingleTake my money!!
8994197558121.01266153628761.071M2nd CycleSingleMeh...
8995198654292.0297210114111361110.031MGraduationTogetherTake my money!!
89961938125962.03875166861225561.045M2nd CycleMarriedTake my money!!
8997199426385.0924465132146151.052M1st CycleSingleKind of OK